Uses of Interface
de.lmu.ifi.dbs.elki.database.Database

Packages that use Database

de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms Clustering algorithms are supposed to implement the Algorithm-Interface.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash	Helper classes for the CASH algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique	Helper classes for the CLIQUE algorithm.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.statistics	Statistical analysis algorithms The algorithms in this package perform statistical analysis of the data (e.g. compute distributions, distance distributions etc.)
de.lmu.ifi.dbs.elki.application.visualization	Visualization applications in ELKI.
de.lmu.ifi.dbs.elki.data.model	Cluster models classes for various algorithms.
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
de.lmu.ifi.dbs.elki.database.connection	Database connections are classes implementing data sources.
de.lmu.ifi.dbs.elki.distance	Distances and (in subpackages) distance functions and similarity functions.
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
de.lmu.ifi.dbs.elki.distance.distancefunction.adapter	Distance functions deriving distances from e.g. similarity measures
de.lmu.ifi.dbs.elki.distance.similarityfunction	Similarity functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.evaluation.histogram	Functionality for the evaluation of algorithms using histograms.
de.lmu.ifi.dbs.elki.evaluation.roc	Evaluation of rankings using ROC AUC (Receiver Operation Characteristics - Area Under Curve)
de.lmu.ifi.dbs.elki.index	Index structure implementations
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants	M-Tree and variants.
de.lmu.ifi.dbs.elki.index.tree.spatial	Tree-based index structures for spatial indexing.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn	RdKNNTree
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
de.lmu.ifi.dbs.elki.preprocessing	Preprocessors used for data preparation in a first step of various algorithms or distance and similarity measures.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
de.lmu.ifi.dbs.elki.result.textwriter	Text serialization (CSV, Gnuplot, Console, ...)
de.lmu.ifi.dbs.elki.utilities	Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
de.lmu.ifi.dbs.elki.utilities.referencepoints	Package containing strategies to obtain reference points Shared code for various algorithms that use reference points.
de.lmu.ifi.dbs.elki.utilities.scaling.outlier	Scaling of Outlier scores, that require a statistical analysis of the occurring values
de.lmu.ifi.dbs.elki.visualization	Visualization package of ELKI.
de.lmu.ifi.dbs.elki.visualization.gui	Package to provide a visualization GUI.
de.lmu.ifi.dbs.elki.visualization.gui.overview	Classes for managing the overview plot.
de.lmu.ifi.dbs.elki.visualization.scales	Scales handling for plotting.
de.lmu.ifi.dbs.elki.visualization.visualizers	Visualizers for various results

Uses of Database in de.lmu.ifi.dbs.elki.algorithm

Methods in de.lmu.ifi.dbs.elki.algorithm with parameters of type Database

protected BitSet[]	APRIORI.frequentItemsets(Map<BitSet,Integer> support, BitSet[] candidates, Database<BitVector> database) Returns the frequent BitSets out of the given BitSets with respect to the given database.
CorrelationAnalysisSolution<V>	DependencyDerivator.generateModel(Database<V> db, Collection<Integer> ids) Runs the pca on the given set of IDs.
CorrelationAnalysisSolution<V>	DependencyDerivator.generateModel(Database<V> db, Collection<Integer> ids, V centroidDV) Runs the pca on the given set of IDs and for the given centroid.
R	Algorithm.run(Database<O> database) Runs the algorithm.
R	AbstractAlgorithm.run(Database<O> database) Calls the runInTime()-method of extending classes.
protected AprioriResult	APRIORI.runInTime(Database<BitVector> database) Performs the APRIORI algorithm on the given database.
protected abstract R	AbstractAlgorithm.runInTime(Database<O> database) The run method encapsulated in measure of runtime.
protected MultiResult	MetaMultiAlgorithm.runInTime(Database<O> database)
protected KNNDistanceOrderResult<D>	KNNDistanceOrder.runInTime(Database<O> database) Provides an order of the kNN-distances for all objects within the specified database.
protected Result	DummyAlgorithm.runInTime(Database<V> database) Iterates over all points in the database.
protected Result	NullAlgorithm.runInTime(Database<V> database) Iterates over all points in the database.
CorrelationAnalysisSolution<V>	DependencyDerivator.runInTime(Database<V> db) Computes quantitatively linear dependencies among the attributes of the given database based on a linear correlation PCA.
protected AnnotationFromHashMap<KNNList<D>>	KNNJoin.runInTime(Database<V> database) Joins in the given spatial database to each object its k-nearest neighbors.
protected CollectionResult<CTriple<Integer,Integer,Double>>	MaterializeDistances.runInTime(Database<V> database) Iterates over all points in the database.

Uses of Database in de.lmu.ifi.dbs.elki.algorithm.clustering

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering with parameters of type Database

protected double	EM.assignProbabilitiesToInstances(Database<V> database, List<Double> normDistrFactor, List<V> means, List<Matrix> invCovMatr, List<Double> clusterWeights, HashMap<Integer,double[]> probClusterIGivenX) Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.
protected void	SNNClustering.expandCluster(Database<O> database, Integer startObjectID, FiniteProgress objprog, IndefiniteProgress clusprog) DBSCAN-function expandCluster adapted to SNN criterion.
protected void	DBSCAN.expandCluster(Database<O> database, Integer startObjectID, FiniteProgress objprog, IndefiniteProgress clusprog) DBSCAN-function expandCluster.
protected void	ProjectedDBSCAN.expandCluster(Database<V> database, Integer startObjectID, FiniteProgress objprog, IndefiniteProgress clusprog) ExpandCluster function of DBSCAN.
protected void	OPTICS.expandClusterOrder(ClusterOrderResult<D> clusterOrder, Database<O> database, Integer objectID, FiniteProgress progress) OPTICS-function expandClusterOrder.
protected List<Integer>	SNNClustering.findSNNNeighbors(Database<O> database, Integer queryObject) Returns the shared nearest neighbors of the specified query object in the given database.
protected List<V>	EM.initialMeans(Database<V> database) Creates k random points distributed uniformly within the attribute ranges of the given database.
protected List<V>	KMeans.means(List<List<Integer>> clusters, List<V> means, Database<V> database) Returns the mean vectors of the given clusters in the given database.
private HashMap<String,Collection<Integer>>	ByLabelClustering.multipleAssignment(Database<O> database) Assigns the objects of the database to multiple clusters according to their labels.
C	ClusteringAlgorithm.run(Database<O> database) Runs the algorithm.
protected Clustering<Model>	SNNClustering.runInTime(Database<O> database) Performs the SNN clustering algorithm on the given database.
protected ClusterOrderResult<D>	DeLiClu.runInTime(Database<O> database) Performs the DeLiClu algorithm on the given database.
protected Clustering<Model>	ByLabelClustering.runInTime(Database<O> database) Run the actual clustering algorithm.
protected Clustering<Model>	DBSCAN.runInTime(Database<O> database) Performs the DBSCAN algorithm on the given database.
protected MultiResult	SLINK.runInTime(Database<O> database) Performs the SLINK algorithm on the given database.
protected ClusterOrderResult<D>	OPTICS.runInTime(Database<O> database) Performs the OPTICS algorithm on the given database.
protected Clustering<Model>	TrivialAllInOne.runInTime(Database<O> database) Run the actual clustering algorithm.
protected Clustering<Model>	TrivialAllNoise.runInTime(Database<O> database) Run the actual clustering algorithm.
protected Clustering<Model>	ByLabelHierarchicalClustering.runInTime(Database<O> database) Run the actual clustering algorithm.
protected Clustering<Model>	ProjectedDBSCAN.runInTime(Database<V> database)
protected Clustering<EMModel<V>>	EM.runInTime(Database<V> database) Performs the EM clustering algorithm on the given database.
protected Clustering<MeanModel<V>>	KMeans.runInTime(Database<V> database) Performs the k-means algorithm on the given database.
private HashMap<String,Collection<Integer>>	ByLabelClustering.singleAssignment(Database<O> database) Assigns the objects of the database to single clusters according to their labels.
protected List<List<Integer>>	KMeans.sort(List<V> means, Database<V> database) Returns a list of clusters.

Uses of Database in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation declared as Database

private Database<ParameterizationFunction>

CASH.database The database holding the objects.

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with type parameters of type Database

private ClassParameter<Database<V>>	COPAC.PARTITION_DB_PARAM Parameter to specify the database class for each partition, must extend Database.
private Class<? extends Database<V>>	COPAC.partitionDatabase Holds the instance of the partition database specified by COPAC.PARTITION_DB_PARAM.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return Database

private Database<ParameterizationFunction>	CASH.buildDB(int dim, Matrix basis, Set<Integer> ids, Database<ParameterizationFunction> database) Builds a dim-1 dimensional database where the objects are projected into the specified subspace.
private Database<DoubleVector>	CASH.buildDerivatorDB(Database<ParameterizationFunction> database, CASHInterval interval) Builds a database for the derivator consisting of the ids in the specified interval.
private Database<DoubleVector>	CASH.buildDerivatorDB(Database<ParameterizationFunction> database, Set<Integer> ids) Builds a database for the derivator consisting of the ids in the specified interval.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with parameters of type Database

private void	ORCLUS.assign(Database<V> database, List<ORCLUS.ORCLUSCluster> clusters) Creates a partitioning of the database by assigning each object to its closest seed.
private Database<ParameterizationFunction>	CASH.buildDB(int dim, Matrix basis, Set<Integer> ids, Database<ParameterizationFunction> database) Builds a dim-1 dimensional database where the objects are projected into the specified subspace.
private Database<DoubleVector>	CASH.buildDerivatorDB(Database<ParameterizationFunction> database, CASHInterval interval) Builds a database for the derivator consisting of the ids in the specified interval.
private Database<DoubleVector>	CASH.buildDerivatorDB(Database<ParameterizationFunction> database, Set<Integer> ids) Builds a database for the derivator consisting of the ids in the specified interval.
private double[]	CASH.determineMinMaxDistance(Database<ParameterizationFunction> database, int dimensionality) Determines the minimum and maximum function value of all parameterization functions stored in the specified database.
private Clustering<Model>	CASH.doRun(Database<ParameterizationFunction> database, FiniteProgress progress) Runs the CASH algorithm on the specified database, this method is recursively called until only noise is left.
private SortedMap<Integer,List<Cluster<CorrelationModel<V>>>>	ERiC.extractCorrelationClusters(Clustering<Model> copacResult, Database<V> database, int dimensionality) Extracts the correlation clusters and noise from the copac result and returns a mapping of correlation dimension to maps of clusters within this correlation dimension.
private Matrix	ORCLUS.findBasis(Database<V> database, ORCLUS.ORCLUSCluster cluster, int dim) Finds the basis of the subspace of dimensionality dim for the specified cluster.
private Set<Integer>	CASH.getDatabaseIDs(Database<ParameterizationFunction> database) Returns the set of ids belonging to the specified database.
private void	CASH.initHeap(DefaultHeap<Integer,CASHInterval> heap, Database<ParameterizationFunction> database, int dim, Set<Integer> ids) Initializes the heap with the root intervals.
private List<ORCLUS.ORCLUSCluster>	ORCLUS.initialSeeds(Database<V> database, int k) Initializes the list of seeds wit a random sample of size k.
private void	ORCLUS.merge(Database<V> database, List<ORCLUS.ORCLUSCluster> clusters, int k_new, int d_new, IndefiniteProgress cprogress) Reduces the number of seeds to k_new
private ORCLUS.ProjectedEnergy	ORCLUS.projectedEnergy(Database<V> database, ORCLUS.ORCLUSCluster c_i, ORCLUS.ORCLUSCluster c_j, int i, int j, int dim) Computes the projected energy of the specified clusters.
private Matrix	CASH.runDerivator(Database<ParameterizationFunction> database, int dim, CASHInterval interval, Set<Integer> ids) Runs the derivator on the specified interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.
private LinearEquationSystem	CASH.runDerivator(Database<ParameterizationFunction> database, int dimensionality, Set<Integer> ids) Runs the derivator on the specified interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.
protected Clustering<Model>	CASH.runInTime(Database<ParameterizationFunction> database) Performs the CASH algorithm on the given database.
protected ClusterOrderResult<PCACorrelationDistance>	HiCO.runInTime(Database<V> database)
protected Clustering<CorrelationModel<V>>	ERiC.runInTime(Database<V> database) Performs the ERiC algorithm on the given database.
protected Clustering<Model>	ORCLUS.runInTime(Database<V> database) Performs the ORCLUS algorithm on the given database.
protected Clustering<Model>	COPAC.runInTime(Database<V> database) Performs the COPAC algorithm on the given database.
private Clustering<Model>	COPAC.runPartitionAlgorithm(Database<V> database, Map<Integer,List<Integer>> partitionMap) Runs the partition algorithm and creates the result.
private ORCLUS.ORCLUSCluster	ORCLUS.union(Database<V> database, ORCLUS.ORCLUSCluster c1, ORCLUS.ORCLUSCluster c2, int dim) Returns the union of the two specified clusters.

Uses of Database in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash declared as Database

private Database<ParameterizationFunction>

CASHIntervalSplit.database The database storing the parameterization functions.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash with parameters of type Database

CASHIntervalSplit(Database<ParameterizationFunction> database, int minPts)
Initializes the logger and sets the debug status to the given value.

Uses of Database in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with parameters of type Database

private Map<Integer,PROCLUS.PROCLUSCluster>	PROCLUS.assignPoints(Map<Integer,Set<Integer>> dimensions, Database<V> database) Assigns the objects to the clusters.
private double	PROCLUS.avgDistance(V centroid, Set<Integer> objectIDs, Database<V> database, int dimension) Computes the average distance of the objects to the centroid along the specified dimension.
private void	DiSH.buildHierarchy(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V>> distanceFunction, List<Cluster<SubspaceModel<V>>> clusters, int dimensionality) Builds the cluster hierarchy
private void	DiSH.checkClusters(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V>> distanceFunction, Map<BitSet,List<Pair<BitSet,DatabaseObjectGroupCollection<List<Integer>>>>> clustersMap) Removes the clusters with size < minpts from the cluster map and adds them to their parents.
private Clustering<SubspaceModel<V>>	DiSH.computeClusters(Database<V> database, ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder) Computes the hierarchical clusters according to the cluster order.
private List<Pair<Subspace<V>,Set<Integer>>>	CLIQUE.determineClusters(Database<V> database, List<CLIQUESubspace<V>> denseSubspaces) Determines the clusters in the specified dense subspaces.
private double	PROCLUS.evaluateClusters(Map<Integer,PROCLUS.PROCLUSCluster> clusters, Map<Integer,Set<Integer>> dimensions, Database<V> database) Evaluates the quality of the clusters.
private Map<BitSet,List<Pair<BitSet,DatabaseObjectGroupCollection<List<Integer>>>>>	DiSH.extractClusters(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V>> distanceFunction, ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder) Extracts the clusters from the cluster order.
private List<CLIQUESubspace<V>>	CLIQUE.findDenseSubspaceCandidates(Database<V> database, List<CLIQUESubspace<V>> denseSubspaces) Determines the k-dimensional dense subspace candidates from the specified (k-1)-dimensional dense subspaces.
private List<CLIQUESubspace<V>>	CLIQUE.findDenseSubspaces(Database<V> database, List<CLIQUESubspace<V>> denseSubspaces) Determines the k-dimensional dense subspaces and performs a pruning if this option is chosen.
private Map<Integer,Set<Integer>>	PROCLUS.findDimensions(Set<Integer> medoids, Database<V> database, Map<Integer,List<DistanceResultPair<DoubleDistance>>> localities) Determines the set of correlated dimensions for each medoid in the specified medoid set.
private List<CLIQUESubspace<V>>	CLIQUE.findOneDimensionalDenseSubspaceCandidates(Database<V> database) Determines the one-dimensional dense subspace candidates by making a pass over the database.
private List<CLIQUESubspace<V>>	CLIQUE.findOneDimensionalDenseSubspaces(Database<V> database) Determines the one dimensional dense subspaces and performs a pruning if this option is chosen.
private Pair<BitSet,DatabaseObjectGroupCollection<List<Integer>>>	DiSH.findParent(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V>> distanceFunction, Pair<BitSet,DatabaseObjectGroupCollection<List<Integer>>> child, Map<BitSet,List<Pair<BitSet,DatabaseObjectGroupCollection<List<Integer>>>>> clustersMap) Returns the parent of the specified cluster
private Map<Integer,List<DistanceResultPair<DoubleDistance>>>	PROCLUS.getLocalities(Set<Integer> m_c, Database<V> database) Computes the localities of the specified medoids.
private Collection<CLIQUEUnit<V>>	CLIQUE.initOneDimensionalUnits(Database<V> database) Initializes and returns the one dimensional units.
private boolean	DiSH.isParent(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V>> distanceFunction, Cluster<SubspaceModel<V>> parent, List<Cluster<SubspaceModel<V>>> children) Returns true, if the specified parent cluster is a parent of one child of the children clusters.
private List<Cluster<Model>>	SUBCLU.runDBSCAN(Database<V> database, List<Integer> ids, Subspace<V> subspace) Runs the DBSCAN algorithm on the specified partition of the database in the given subspace.
protected Clustering<SubspaceModel<V>>	DiSH.runInTime(Database<V> database) Performs the DiSH algorithm on the given database.
protected Clustering<Model>	PROCLUS.runInTime(Database<V> database) Performs the PROCLUS algorithm on the given database.
protected ClusterOrderResult<PreferenceVectorBasedCorrelationDistance>	HiSC.runInTime(Database<V> database)
protected Clustering<SubspaceModel<V>>	CLIQUE.runInTime(Database<V> database) Performs the CLIQUE algorithm on the given database.
protected Clustering<SubspaceModel<V>>	SUBCLU.runInTime(Database<V> database) Performs the SUBCLU algorithm on the given database.

Uses of Database in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique with parameters of type Database

List<Pair<Subspace<V>,Set<Integer>>>

CLIQUESubspace.determineClusters(Database<V> database) Determines all clusters in this subspace by performing a depth-first search algorithm to find connected dense units.

Uses of Database in de.lmu.ifi.dbs.elki.algorithm.outlier

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier with parameters of type Database

private PriorityQueue<FCPair<Double,Integer>>	ABOD.calcDistsandNN(Database<V> data, KernelMatrix<V> kernelMatrix, int sampleSize, Integer aKey, HashMap<Integer,Double> dists)
private PriorityQueue<FCPair<Double,Integer>>	ABOD.calcDistsandRNDSample(Database<V> data, KernelMatrix<V> kernelMatrix, int sampleSize, Integer aKey, HashMap<Integer,Double> dists)
List<DistanceResultPair<DoubleDistance>>	ReferenceBasedOutlierDetection.computeDistanceVector(V refPoint, Database<V> database) Computes for each object the distance to one reference point.
protected HashMap<Integer,Double>	DBOutlierDetection.computeOutlierScores(Database<O> database, D neighborhoodSize)
protected HashMap<Integer,Double>	DBOutlierScore.computeOutlierScores(Database<O> database, D d)
protected abstract HashMap<Integer,Double>	AbstractDBOutlier.computeOutlierScores(Database<O> database, D d) computes an outlier score for each object of the database.
Collection<V>	ReferenceBasedOutlierDetection.computeReferencePoints(Database<V> database) Computes the reference points.
private void	ABOD.generateExplanation(Database<V> data, Integer key, LinkedList<Integer> expList)
void	ABOD.getExplanations(Database<V> data) Get explanations for points in the database.
OutlierResult	ABOD.getFastRanking(Database<V> database, int k, int sampleSize) Main part of the algorithm.
private KNNList<DoubleDistance>	SOD.getKNN(Database<V> database, Integer queryObject) Provides the k nearest neighbors in terms of the shared nearest neighbor distance.
OutlierResult	ABOD.getRanking(Database<V> database, int k) Main part of the algorithm.
private double	GaussianUniformMixture.loglikelihoodNormal(Collection<Integer> objids, Database<V> database) Computes the loglikelihood of all normal objects.
protected OutlierResult	LOCI.runInTime(Database<O> database) Runs the algorithm in the timed evaluation part.
protected MultiResult	OPTICSOF.runInTime(Database<O> database)
protected OutlierResult	LoOP.runInTime(Database<O> database) Performs the LoOP algorithm on the given database.
protected OutlierResult	AbstractDBOutlier.runInTime(Database<O> database) Runs the algorithm in the timed evaluation part.
protected MultiResult	INFLO.runInTime(Database<O> database)
protected OutlierResult	LDOF.runInTime(Database<O> database)
protected OutlierResult	KNNWeightOutlier.runInTime(Database<O> database) Runs the algorithm in the timed evaluation part.
protected OutlierResult	KNNOutlier.runInTime(Database<O> database) Runs the algorithm in the timed evaluation part.
protected OutlierResult	LOF.runInTime(Database<O> database) Performs the Generalized LOF_SCORE algorithm on the given database.
protected OutlierResult	ReferenceBasedOutlierDetection.runInTime(Database<V> database) Runs the algorithm in the timed evaluation part.
protected OutlierResult	EMOutlier.runInTime(Database<V> database) Runs the algorithm in the timed evaluation part.
protected OutlierResult	GaussianModel.runInTime(Database<V> database)
protected OutlierResult	GaussianUniformMixture.runInTime(Database<V> database)
protected OutlierResult	SOD.runInTime(Database<V> database) Performs the SOD algorithm on the given database.
protected OutlierResult	ABOD.runInTime(Database<V> database)

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier with parameters of type Database

SOD.SODModel(Database<O> database, List<Integer> neighborhood, double alpha, O queryObject)
Initialize SOD Model

Uses of Database in de.lmu.ifi.dbs.elki.algorithm.statistics

Methods in de.lmu.ifi.dbs.elki.algorithm.statistics with parameters of type Database

private DoubleMinMax	DistanceStatisticsWithClasses.exactMinMax(Database<V> database, DistanceFunction<V,D> distFunc)
protected CollectionResult<DoubleVector>	EvaluateRankingQuality.runInTime(Database<V> database) Run the algorithm.
protected CollectionResult<DoubleVector>	RankingQualityHistogram.runInTime(Database<V> database) Run the algorithm.
protected CollectionResult<DoubleVector>	DistanceStatisticsWithClasses.runInTime(Database<V> database) Iterates over all points in the database.
private DoubleMinMax	DistanceStatisticsWithClasses.sampleMinMax(Database<V> database, DistanceFunction<V,D> distFunc)

Uses of Database in de.lmu.ifi.dbs.elki.application.visualization

Fields in de.lmu.ifi.dbs.elki.application.visualization declared as Database

protected Database<O>

KNNExplorer.ExplorerWindow.db

Methods in de.lmu.ifi.dbs.elki.application.visualization with parameters of type Database

void	KNNExplorer.ExplorerWindow.run(Database<O> db, DistanceFunction<O,D> distanceFunction) Process the given Database and distance function.

Uses of Database in de.lmu.ifi.dbs.elki.data.model

Fields in de.lmu.ifi.dbs.elki.data.model declared as Database

private Database<V>

Bicluster.database The database this bicluster is defined for.

Methods in de.lmu.ifi.dbs.elki.data.model that return Database

Database<V>

Bicluster.getDatabase() Getter to retrieve the database

Constructors in de.lmu.ifi.dbs.elki.data.model with parameters of type Database

Bicluster(int[] rowIDs, int[] colIDs, Database<V> database)
Defines a new bicluster for given parameters.

BiclusterWithInverted(int[] rowIDs, int[] colIDs, Database<V> database)

CorrelationAnalysisSolution(LinearEquationSystem solution, Database<V> db, Matrix strongEigenvectors, Matrix weakEigenvectors, Matrix similarityMatrix, Vector centroid)
Provides a new CorrelationAnalysisSolution holding the specified matrix.

CorrelationAnalysisSolution(LinearEquationSystem solution, Database<V> db, Matrix strongEigenvectors, Matrix weakEigenvectors, Matrix similarityMatrix, Vector centroid, NumberFormat nf)
Provides a new CorrelationAnalysisSolution holding the specified matrix and number format.

Uses of Database in de.lmu.ifi.dbs.elki.database

Classes in de.lmu.ifi.dbs.elki.database that implement Database

class	AbstractDatabase<O extends DatabaseObject> Provides a mapping for associations based on a Hashtable and functions to get the next usable ID for insertion, making IDs reusable after deletion of the entry.
class	IndexDatabase<O extends DatabaseObject> IndexDatabase is a database implementation which is supported by an index structure.
class	MetricalIndexDatabase<O extends DatabaseObject,D extends Distance<D>,N extends MetricalNode<N,E>,E extends MTreeEntry<D>> MetricalIndexDatabase is a database implementation which is supported by a metrical index structure.
class	SequentialDatabase<O extends DatabaseObject> SequentialDatabase is a simple implementation of a Database.
class	SpatialIndexDatabase<O extends NumberVector<O,?>,N extends SpatialNode<N,E>,E extends SpatialEntry> SpatialIndexDatabase is a database implementation which is supported by a spatial index structure.

Methods in de.lmu.ifi.dbs.elki.database that return Database

<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel> Database<O>	LabelsFromClustering.makeDatabaseFromClustering(Database<O> olddb, R clustering, Class<L> classLabel) Retrieve a cloned database that - does not contain noise points - has labels assigned based on the given clustering Useful for e.g. training a classifier based on a clustering.
Database<O>	Database.partition(List<Integer> ids) Returns a partition of this database according to the specified Lists of IDs.
Database<O>	AbstractDatabase.partition(List<Integer> ids)

Methods in de.lmu.ifi.dbs.elki.database that return types with arguments of type Database

<O extends DatabaseObject,R extends Clustering<M>,M extends Model> Map<Cluster<M>,Database<O>>	PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering) Use an existing clustering to partition a database.
<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel> Map<L,Database<O>>	PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering, Class<L> classLabel) Use an existing clustering to partition a database.
Map<Integer,Database<O>>	Database.partition(Map<Integer,List<Integer>> partitions) Returns a Map of partition IDs to Databases according to the specified Map of partition IDs to Lists of IDs.
Map<Integer,Database<O>>	AbstractDatabase.partition(Map<Integer,List<Integer>> partitions)
Map<Integer,Database<O>>	Database.partition(Map<Integer,List<Integer>> partitions, Class<? extends Database<O>> dbClass, Collection<Pair<OptionID,Object>> dbParameters) Returns a Map of partition IDs to Databases of the specified class according to the specified Map of partition IDs to Lists of IDs.
Map<Integer,Database<O>>	AbstractDatabase.partition(Map<Integer,List<Integer>> partitions, Class<? extends Database<O>> dbClass, Collection<Pair<OptionID,Object>> dbParameters)

Methods in de.lmu.ifi.dbs.elki.database with parameters of type Database

<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel> Database<O>	LabelsFromClustering.makeDatabaseFromClustering(Database<O> olddb, R clustering, Class<L> classLabel) Retrieve a cloned database that - does not contain noise points - has labels assigned based on the given clustering Useful for e.g. training a classifier based on a clustering.
<O extends DatabaseObject,R extends Clustering<M>,M extends Model> Map<Cluster<M>,Database<O>>	PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering) Use an existing clustering to partition a database.
<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel> Map<L,Database<O>>	PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering, Class<L> classLabel) Use an existing clustering to partition a database.

Method parameters in de.lmu.ifi.dbs.elki.database with type arguments of type Database

Map<Integer,Database<O>>	Database.partition(Map<Integer,List<Integer>> partitions, Class<? extends Database<O>> dbClass, Collection<Pair<OptionID,Object>> dbParameters) Returns a Map of partition IDs to Databases of the specified class according to the specified Map of partition IDs to Lists of IDs.
Map<Integer,Database<O>>	AbstractDatabase.partition(Map<Integer,List<Integer>> partitions, Class<? extends Database<O>> dbClass, Collection<Pair<OptionID,Object>> dbParameters)

Constructors in de.lmu.ifi.dbs.elki.database with parameters of type Database

DatabaseEvent(Database<? extends DatabaseObject> source, List<Integer> objectIDs)
Used to create an event when database objects have been updated, inserted, or removed.

Uses of Database in de.lmu.ifi.dbs.elki.database.connection

Fields in de.lmu.ifi.dbs.elki.database.connection declared as Database

(package private) Database<O>

AbstractDatabaseConnection.database Holds the instance of the database specified by AbstractDatabaseConnection.DATABASE_PARAM.

Fields in de.lmu.ifi.dbs.elki.database.connection with type parameters of type Database

private ObjectParameter<Database<O>>

AbstractDatabaseConnection.DATABASE_PARAM Parameter to specify the database to be provided by the parse method, must extend Database.

Methods in de.lmu.ifi.dbs.elki.database.connection that return Database

Database<MultiRepresentedObject<O>>	MultipleFileBasedDatabaseConnection.getDatabase(Normalization<MultiRepresentedObject<O>> normalization)
Database<O>	InputStreamDatabaseConnection.getDatabase(Normalization<O> normalization)
Database<O>	DatabaseConnection.getDatabase(Normalization<O> normalization) Returns a Database according to parameter settings.
Database<O>	EmptyDatabaseConnection.getDatabase(Normalization<O> normalization)

Uses of Database in de.lmu.ifi.dbs.elki.distance

Fields in de.lmu.ifi.dbs.elki.distance declared as Database

private Database<O>

AbstractMeasurementFunction.database The database that holds the objects for which the measurements should be computed.

Methods in de.lmu.ifi.dbs.elki.distance that return Database

protected Database<O>

AbstractMeasurementFunction.getDatabase() Returns the database holding the objects for which the measurements should be computed.

Methods in de.lmu.ifi.dbs.elki.distance with parameters of type Database

void	AbstractMeasurementFunction.setDatabase(Database<O> database)
void	MeasurementFunction.setDatabase(Database<O> database) Set the database that holds the associations for the DatabaseObject for which the measurements should be computed.

Uses of Database in de.lmu.ifi.dbs.elki.distance.distancefunction

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction with parameters of type Database

void	AbstractPreprocessorBasedDistanceFunction.setDatabase(Database<O> database) Calls AbstractMeasurementFunction.setDatabase(de.lmu.ifi.dbs.elki.database.Database) and runs the preprocessor on the database.
void	AbstractLocallyWeightedDistanceFunction.setDatabase(Database<O> database)
void	KernelBasedLocallyWeightedDistanceFunction.setDatabase(Database<V> database)

Uses of Database in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter with parameters of type Database

void	SimilarityAdapterAbstract.setDatabase(Database<V> database)

Uses of Database in de.lmu.ifi.dbs.elki.distance.similarityfunction

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction with parameters of type Database

void	AbstractPreprocessorBasedSimilarityFunction.setDatabase(Database<O> database) Calls AbstractMeasurementFunction.setDatabase(de.lmu.ifi.dbs.elki.database.Database) and runs the preprocessor on the database.

Uses of Database in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with parameters of type Database

void	ArbitraryKernelFunctionWrapper.setDatabase(Database<O> database)

Constructors in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with parameters of type Database

KernelMatrix(KernelFunction<O,DoubleDistance> kernelFunction, Database<O> database)
Provides a new kernel matrix.

KernelMatrix(KernelFunction<O,DoubleDistance> kernelFunction, Database<O> database, List<Integer> ids)
Provides a new kernel matrix.

Uses of Database in de.lmu.ifi.dbs.elki.evaluation.histogram

Methods in de.lmu.ifi.dbs.elki.evaluation.histogram with parameters of type Database

private OutlierResult	ComputeOutlierHistogram.getOutlierResult(Database<O> database, Result result) Find an OutlierResult to work with.
protected MultiResult	ComputeOutlierHistogram.runInTime(Database<O> database)

Uses of Database in de.lmu.ifi.dbs.elki.evaluation.roc

Methods in de.lmu.ifi.dbs.elki.evaluation.roc with parameters of type Database

private Iterator<Integer>	ComputeROCCurve.getIterableResult(Database<O> database, Result result) Find an "iterable" result that looks like object IDs.
protected MultiResult	ComputeROCCurve.runInTime(Database<O> database)

Uses of Database in de.lmu.ifi.dbs.elki.index

Methods in de.lmu.ifi.dbs.elki.index with parameters of type Database

void	Index.setDatabase(Database<O> database) Sets the database in the distance function of this index (if existing).

Uses of Database in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants with parameters of type Database

void	AbstractMTree.setDatabase(Database<O> database)

Uses of Database in de.lmu.ifi.dbs.elki.index.tree.spatial

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial with parameters of type Database

void	SpatialIndex.setDatabase(Database<O> database) Sets the database in the distance function of this index (if existing).

Uses of Database in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn with parameters of type Database

void	RdKNNTree.setDatabase(Database<O> database) Sets the database in the distance function of this index.

Uses of Database in de.lmu.ifi.dbs.elki.math.linearalgebra.pca

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with parameters of type Database

PCAResult	PCARunner.processDatabase(Database<V> database) Run PCA on the complete database
Matrix	StandardCovarianceMatrixBuilder.processDatabase(Database<V> database) Compute Covariance Matrix for a complete database
Matrix	CovarianceMatrixBuilder.processDatabase(Database<V> database) Compute Covariance Matrix for a complete database
PCAFilteredResult	PCAFilteredRunner.processIds(Collection<Integer> ids, Database<V> database) Run PCA on a collection of database IDs
PCAResult	PCARunner.processIds(Collection<Integer> ids, Database<V> database) Run PCA on a collection of database IDs
Matrix	WeightedCovarianceMatrixBuilder.processIds(Collection<Integer> ids, Database<V> database) Weighted Covariance Matrix for a set of IDs.
Matrix	KernelCovarianceMatrixBuilder.processIds(Collection<Integer> ids, Database<V> database) Returns the local kernel matrix of the specified ids.
Matrix	StandardCovarianceMatrixBuilder.processIds(Collection<Integer> ids, Database<V> database) Compute Covariance Matrix for a collection of database IDs
abstract Matrix	CovarianceMatrixBuilder.processIds(Collection<Integer> ids, Database<V> database) Compute Covariance Matrix for a collection of database IDs
PCAFilteredResult	PCAFilteredRunner.processQueryResult(Collection<DistanceResultPair<D>> results, Database<V> database) Run PCA on a QueryResult Collection
PCAResult	PCARunner.processQueryResult(Collection<DistanceResultPair<D>> results, Database<V> database) Run PCA on a QueryResult Collection
Matrix	CovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Database<V> database) Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds
Matrix	WeightedCovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Database<V> database, int k) Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds
Matrix	CovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Database<V> database, int k) Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds

Uses of Database in de.lmu.ifi.dbs.elki.preprocessing

Fields in de.lmu.ifi.dbs.elki.preprocessing declared as Database

private Database<O>

PreprocessorHandler.database The database to run the preprocessor on.

Methods in de.lmu.ifi.dbs.elki.preprocessing with parameters of type Database

private BitSet	HiSCPreprocessor.determinePreferenceVector(Database<V> database, Integer id, List<Integer> neighborIDs, StringBuffer msg) Determines the preference vector according to the specified neighbor ids.
private BitSet	DiSHPreprocessor.determinePreferenceVector(Database<V> database, Set<Integer>[] neighborIDs, StringBuffer msg) Determines the preference vector according to the specified neighbor ids.
private BitSet	DiSHPreprocessor.determinePreferenceVectorByApriori(Database<V> database, Set<Integer>[] neighborIDs, StringBuffer msg) Determines the preference vector with the apriori strategy.
private SpatialIndexDatabase<O,N,E>	SpatialApproximationMaterializeKNNPreprocessor.getSpatialDatabase(Database<O> database) Do some (limited) type checking, then cast the database into a spatial database.
private DimensionSelectingDistanceFunction<V>[]	DiSHPreprocessor.initDistanceFunctions(Database<V> database, int dimensionality) Initializes the dimension selecting distancefunctions to determine the preference vectors.
protected abstract List<DistanceResultPair<DoubleDistance>>	LocalPCAPreprocessor.objectsForPCA(Integer id, Database<V> database) Returns the objects to be considered within the PCA for the specified query object.
protected List<DistanceResultPair<DoubleDistance>>	RangeQueryBasedLocalPCAPreprocessor.objectsForPCA(Integer id, Database<V> database)
protected List<DistanceResultPair<DoubleDistance>>	KnnQueryBasedLocalPCAPreprocessor.objectsForPCA(Integer id, Database<V> database)
void	Preprocessor.run(Database<O> database, boolean verbose, boolean time) This method executes the particular preprocessing step of this Preprocessor for the objects of the specified database.
void	MaterializeKNNPreprocessor.run(Database<O> database, boolean verbose, boolean time) Annotates the nearest neighbors based on the values of MaterializeKNNPreprocessor.k and MaterializeKNNPreprocessor.distanceFunction to each database object.
void	SharedNearestNeighborsPreprocessor.run(Database<O> database, boolean verbose, boolean time) Annotates the nearest neighbors based on the values of SharedNearestNeighborsPreprocessor.numberOfNeighbors and SharedNearestNeighborsPreprocessor.distanceFunction to each database object.
void	SpatialApproximationMaterializeKNNPreprocessor.run(Database<O> database, boolean verbose, boolean time) Annotates the nearest neighbors based on the values of MaterializeKNNPreprocessor.k and MaterializeKNNPreprocessor.distanceFunction to each database object.
void	DiSHPreprocessor.run(Database<V> database, boolean verbose, boolean time)
void	LocalPCAPreprocessor.run(Database<V> database, boolean verbose, boolean time) Performs for each object of the specified database a filtered PCA based on the local neighborhood of the object.
void	HiSCPreprocessor.run(Database<V> database, boolean verbose, boolean time)
void	ProjectedDBSCANPreprocessor.run(Database<V> database, boolean verbose, boolean time)
void	PreprocessorHandler.runPreprocessor(Database<O> database) Runs the preprocessor on the database if the omit flag is not set or the database does contain the association id neither for any id nor as global association.
protected void	FourCPreprocessor.runVarianceAnalysis(Integer id, List<DistanceResultPair<D>> neighbors, Database<V> database) This method implements the type of variance analysis to be computed for a given point.
protected abstract void	ProjectedDBSCANPreprocessor.runVarianceAnalysis(Integer id, List<DistanceResultPair<D>> neighbors, Database<V> database) This method implements the type of variance analysis to be computed for a given point.
protected void	PreDeConPreprocessor.runVarianceAnalysis(Integer id, List<DistanceResultPair<D>> neighbors, Database<V> database) TODO provide correct commentary This method implements the type of variance analysis to be computed for a given point.

Uses of Database in de.lmu.ifi.dbs.elki.result

Fields in de.lmu.ifi.dbs.elki.result declared as Database

private Database<O>	AnnotationFromDatabase.database database storage
protected Database<O>	OrderingFromAssociation.db Database

Methods in de.lmu.ifi.dbs.elki.result that return Database

Database<O>

AnnotationFromDatabase.getDatabase() Return database referenced by annotations

Methods in de.lmu.ifi.dbs.elki.result with parameters of type Database

void	ResultHandler.processResult(Database<O> db, R result) Process a result.
void	DiscardResultHandler.processResult(Database<O> db, R result) Process the result... by discarding
void	ResultWriter.processResult(Database<O> db, Result result) Process a single result.

Constructors in de.lmu.ifi.dbs.elki.result with parameters of type Database

AnnotationFromDatabase(Database<O> db, AssociationID<T> association)
Constructor

OrderingFromAssociation(Database<O> db, AssociationID<T> association)
Deprecated.

OrderingFromAssociation(Database<O> db, AssociationID<T> association, boolean descending)
Deprecated.

OrderingFromAssociation(Database<O> db, AssociationID<T> association, Comparator<T> comparator, boolean descending)
Deprecated.

Uses of Database in de.lmu.ifi.dbs.elki.result.textwriter

Methods in de.lmu.ifi.dbs.elki.result.textwriter with parameters of type Database

void	TextWriter.output(Database<O> db, Result r, StreamFactory streamOpener) Stream output.
protected void	TextWriter.printSettings(Database<O> db, TextWriterStream out, List<SettingsResult> sr) Writes a header providing information concerning the underlying database and the specified parameter-settings.
private void	TextWriter.writeGroupResult(Database<O> db, StreamFactory streamOpener, DatabaseObjectGroup group, List<AnnotationResult<?>> ra, List<OrderingResult> ro, NamingScheme naming, List<SettingsResult> sr)
private void	TextWriter.writeIterableResult(Database<O> db, StreamFactory streamOpener, IterableResult<?> ri, MultiResult mr, List<SettingsResult> sr)
private void	TextWriter.writeOtherResult(Database<O> db, StreamFactory streamOpener, Result r, List<SettingsResult> rs)

Uses of Database in de.lmu.ifi.dbs.elki.utilities

Methods in de.lmu.ifi.dbs.elki.utilities with parameters of type Database

static <O extends NumberVector<O,?>> O	DatabaseUtil.centroid(Database<O> database) Returns the centroid as a NumberVector object of the specified database.
static <V extends NumberVector<V,?>> V	DatabaseUtil.centroid(Database<V> database, Collection<Integer> ids) Returns the centroid as a NumberVector object of the specified objects stored in the given database.
static <V extends NumberVector<V,?>> V	DatabaseUtil.centroid(Database<V> database, Collection<Integer> ids, BitSet dimensions) Returns the centroid w.r.t. the dimensions specified by the given BitSet as a NumberVector object of the specified objects stored in the given database.
static <V extends NumberVector<V,?>> V	DatabaseUtil.centroid(Database<V> database, Iterator<Integer> iter, BitSet bitSet) Returns the centroid w.r.t. the dimensions specified by the given BitSet as a NumberVector object of the specified objects stored in the given database.
static <NV extends NumberVector<NV,?>> Pair<NV,NV>	DatabaseUtil.computeMinMax(Database<NV> database) Determines the minimum and maximum values in each dimension of all objects stored in the given database.
static <O extends NumberVector<O,?>> Matrix	DatabaseUtil.covarianceMatrix(Database<O> database) Determines the covariance matrix of the objects stored in the given database.
static <O extends NumberVector<O,?>> Matrix	DatabaseUtil.covarianceMatrix(Database<O> database, O centroid) Determines the covariance matrix of the objects stored in the given database w.r.t. the given centroid.
static <V extends NumberVector<V,?>> Matrix	DatabaseUtil.covarianceMatrix(Database<V> database, Collection<Integer> ids) Determines the covariance matrix of the objects stored in the given database.
static <O extends DatabaseObject> Class<? extends DatabaseObject>	DatabaseUtil.getBaseObjectClassExpensive(Database<O> database) Do a full inspection of the database to find the base object class.
static SortedSet<ClassLabel>	DatabaseUtil.getClassLabels(Database<?> database) Retrieves all class labels within the database.
static String	DatabaseUtil.getClassOrObjectLabel(Database<?> database, Integer objid) Get the class label or object label of an object in the database
static Collection<Integer>	DatabaseUtil.getObjectsByLabelMatch(Database<?> database, Pattern name_pattern) Find object by matching their labels.
static <O extends DatabaseObject> Class<? extends O>	DatabaseUtil.guessObjectClass(Database<O> database) Do a cheap guess at the databases object class.
static double[]	DatabaseUtil.variances(Database<NumberVector<?,?>> database, NumberVector<?,?> centroid, Collection<Integer>[] ids) Determines the variances in each dimension of the specified objects stored in the given database.
static <O extends NumberVector<O,?>> double[]	DatabaseUtil.variances(Database<O> database) Determines the variances in each dimension of all objects stored in the given database.
static <V extends NumberVector<V,?>> double[]	DatabaseUtil.variances(Database<V> database, Collection<Integer> ids) Determines the variances in each dimension of the specified objects stored in the given database.
static <V extends NumberVector<V,?>> double[]	DatabaseUtil.variances(Database<V> database, V centroid, Collection<Integer> ids) Determines the variances in each dimension of the specified objects stored in the given database.

Uses of Database in de.lmu.ifi.dbs.elki.utilities.referencepoints

Fields in de.lmu.ifi.dbs.elki.utilities.referencepoints declared as Database

(package private) Database<O>

FullDatabaseReferencePoints.DatabaseProxy.db The database we query

Methods in de.lmu.ifi.dbs.elki.utilities.referencepoints with parameters of type Database

Collection<O>	RandomSampleReferencePoints.getReferencePoints(Database<O> db)
Collection<O>	AxisBasedReferencePoints.getReferencePoints(Database<O> db)
Collection<O>	RandomGeneratedReferencePoints.getReferencePoints(Database<O> db)
Collection<O>	StarBasedReferencePoints.getReferencePoints(Database<O> db)
Collection<O>	ReferencePointsHeuristic.getReferencePoints(Database<O> db) Get the reference points for the given database.
Collection<O>	FullDatabaseReferencePoints.getReferencePoints(Database<O> db)
Collection<O>	GridBasedReferencePoints.getReferencePoints(Database<O> db)

Constructors in de.lmu.ifi.dbs.elki.utilities.referencepoints with parameters of type Database

FullDatabaseReferencePoints.DatabaseProxy(Database<O> db)
Constructor.

Uses of Database in de.lmu.ifi.dbs.elki.utilities.scaling.outlier

Methods in de.lmu.ifi.dbs.elki.utilities.scaling.outlier with parameters of type Database

private static double	MultiplicativeInverseScaling.getScaleValue(Database<?> db, OutlierResult or) Compute the scaling value in a linear scan over the annotation.
void	MultiplicativeInverseScaling.prepare(Database<?> db, Result result, OutlierResult or)
void	MinusLogGammaScaling.prepare(Database<?> db, Result result, OutlierResult or)
void	SqrtStandardDeviationScaling.prepare(Database<?> db, Result result, OutlierResult or)
void	TopKOutlierScaling.prepare(Database<?> db, Result result, OutlierResult or)
void	OutlierScalingFunction.prepare(Database<?> db, Result result, OutlierResult or) Prepare is called once for each data set, before getScaled() will be called.
void	OutlierLinearScaling.prepare(Database<?> db, Result result, OutlierResult or)
void	OutlierGammaScaling.prepare(Database<?> db, Result result, OutlierResult or)
void	OutlierMinusLogScaling.prepare(Database<?> db, Result result, OutlierResult or)
void	MinusLogStandardDeviationScaling.prepare(Database<?> db, Result result, OutlierResult or)
void	OutlierSqrtScaling.prepare(Database<?> db, Result result, OutlierResult or)
void	StandardDeviationScaling.prepare(Database<?> db, Result result, OutlierResult or)

Uses of Database in de.lmu.ifi.dbs.elki.visualization

Constructors in de.lmu.ifi.dbs.elki.visualization with parameters of type Database

VisualizationProjection(Database<? extends NumberVector<?,?>> db, LinearScale[] scales, AffineTransformation proj)
Constructor with a given database and axes.

VisualizationProjection(Database<? extends NumberVector<?,?>> db, LinearScale[] scales, int ax1, int ax2)
Constructor with a given database and axes.

Uses of Database in de.lmu.ifi.dbs.elki.visualization.gui

Methods in de.lmu.ifi.dbs.elki.visualization.gui with parameters of type Database

void	ResultVisualizer.processResult(Database<DatabaseObject> db, Result result)

Constructors in de.lmu.ifi.dbs.elki.visualization.gui with parameters of type Database

ResultWindow(String title, Database<? extends DatabaseObject> db, MultiResult result, int maxdim)
Constructor.

Uses of Database in de.lmu.ifi.dbs.elki.visualization.gui.overview

Fields in de.lmu.ifi.dbs.elki.visualization.gui.overview declared as Database

private Database<? extends DatabaseObject>

OverviewPlot.db Database we work on.

Methods in de.lmu.ifi.dbs.elki.visualization.gui.overview that return Database

private Database<NV>

OverviewPlot.uglyCastDatabase() Ugly cast of the database to a default number vector.

Constructors in de.lmu.ifi.dbs.elki.visualization.gui.overview with parameters of type Database

OverviewPlot(Database<? extends DatabaseObject> db, MultiResult result, int maxdim)
Constructor.

Uses of Database in de.lmu.ifi.dbs.elki.visualization.scales

Methods in de.lmu.ifi.dbs.elki.visualization.scales with parameters of type Database

static <O extends NumberVector<?,? extends Number>> LinearScale[]

Scales.calcScales(Database<O> db) Compute a linear scale for each dimension.

Uses of Database in de.lmu.ifi.dbs.elki.visualization.visualizers

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers declared as Database

private Database<?>

VisualizerContext.database The database

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers that return Database

<O extends DatabaseObject> Database<O>

VisualizerContext.getDatabase() Get the database itself

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers with parameters of type Database

String	VisualizersForResult.getTitle(Database<? extends DatabaseObject> db, MultiResult result) Try to automatically generate a title for this.
void	VisualizersForResult.processResult(Database<? extends DatabaseObject> db, MultiResult result) Process a particular result.

Constructors in de.lmu.ifi.dbs.elki.visualization.visualizers with parameters of type Database

VisualizerContext(Database<?> database, Result result)
Constructor.